Estimación de biomasa aérea de <i>Eucalyptus grandis</i> y <i>Pinus</i> spp. usando imágenes Sentinel1A y Sentinel2A en Colombia

Estimating aboveground biomass of <i>Eucalyptus grandis</i> and <i>Pinus</i> spp. using Sentinel-1A and Sentinel-2A images in Colombia

  • Adriana Lizeth Tovar Blanco Universidad Nacional de Colombia https://orcid.org/0000-0002-1167-4807
  • Iván Alberto Lizarazo Salcedo Universidad Nacional de Colombia
  • Nelly Rodríguez Eraso Universidad Nacional de Colombia
Palabras clave: Remote sensing, comercial forest plantation, Random Forest, Sentinel, C-band, GLCM, Vegetation index, textures, AGB (en_US)
Palabras clave: Percepción remota, plantación forestal comercial, Random Forest, Sentinel, banda C, GLCM (es_ES)

Resumen (es_ES)

La estimación de la biomasa aérea usando sistemas de aprendizaje automático es útil para conocer de forma rápida y sistemática la productividad en bosques y plantaciones. En este estudio la biomasa aérea (AGB) se estimó para las plantaciones forestales de Eucalyptus grandis y Pinus spp. ubicadas en el sector centro-oriental del departamento del Cauca (Colombia). Las variables de mayor incidencia en AGB para E. grandis fueron las bandas SWIR y las texturas de la polarización VV; mientras que para P. spp fueron CorrelaciónVV, GNDVI y B2. Los modelos obtenidos combinando datos ópticos y SAR muestran mejores resultados con un coeficiente de determinación R2 = 0.27 y un error cuadrado promedio EMC = 42.75 t.ha-1 en E. grandis, y R2 = 0.36 y EMC = 141.71 t.ha-1 en Pinus spp. El estudio demostró el potencial de combinar datos Sentinel para estimar la AGB en plantaciones comerciales y el uso de Randon forest para la construcción de los modelos, pero aún se requiere el estudio del acoplamiento espacial de los datos de campo y su incidencia en las estimaciones de los modelos, así como la pertinencia de adelantar estudios a nivel de especies para evaluar su incertidumbre.  

Resumen (en_US)

Aboveground biomass estimation, using machine-learning systems, is useful for rapid and systematic knowledge of productivity in forests and plantations. In this study, forest aboveground biomass (AGB) was estimated for plantations of Eucalyptus grandis and Pinus spp located in the central-eastern sector of the department of Cauca (Colombia). The variables with the highest incidence in AGB for E. grandis were the SWIR bands and the VV polarization textures, while for Pinus spp. were Correlationvv, GNDVI and B2. The models obtained by combining optical data and SAR show better results with a determination coefficient R2 = 0.27 and an average square error EMC = 42.75 t.ha-1 in E. grandis, and R2 = 0.36 and EMC = 141.71 t.ha-1 in Pinus spp. The study demonstrated the potential of combining Sentinel data to estimate AGB in commercial plantations and the use of Randon forest for model construction.

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Cómo citar
Tovar Blanco, A. L., Lizarazo Salcedo, I. A., & Rodríguez Eraso, N. (2020). Estimación de biomasa aérea de <i>Eucalyptus grandis</i> y <i>Pinus</i&gt; spp. usando imágenes Sentinel1A y Sentinel2A en Colombia. Colombia Forestal, 23(1). https://doi.org/10.14483/2256201X.14854
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